Assessing socio-technical challenges of implementing predictive digital twins for existing housing and cities in the UK

However, their adoption remains limited due to fragmented data systems, low digital literacy, ethical uncertainties, and high implementation costs. While DTs are advancing in commercial construction, their use in residential maintenance remains underexplored.<br><br>This study adopts a socio-technical systems approach to evaluate how predictive DTs can be integrated sustainably and ethically within residential housing, aligning with the human-centric principles of Industry 5.0.

A two-phase qualitative study was conducted between April 2024 and February 2025, involving semi-structured interviews with 26 professionals across public housing, facilities and asset management, BIM, and construction sectors. Thematic analysis identified key barriers and opportunities across four ST pillars: Organisation (limited digital skills, resistance to innovation), Users (ethical concerns and low engagement), Technology (scalability and infrastructure limitations), and Processes (fragmented, reactive maintenance systems).<br><br>These findings informed the development of the STPDT framework, a pragmatic model designed to guide stakeholders from small and medium-sized enterprises to policymakers towards the ethical, inclusive, and collaborative deployment of DTs.